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Limiting games of multi-agent multi-state problems Host Publication: Workshop on Adaptive and Learning Agents 2007 Proceedings Authors: P. Vrancx, K. Verbeeck and A. Nowé Publication Year: 2007 Number of Pages: 5
Abstract: Abstract.We propose to
analyse the behaviour of learning
agents in a multi-state environment by
approximating the problem with a limiting single state game.
The limiting game views each joint agent policy as a single play between
players using the agents' policies as their actions. The payoff given
to each player is the expected reward for the corresponding
agent under the resulting joint policy. In the settings we explore agents
are fully ignorant, i.e. they can only observe themselves they dont know
how many other agents are present in the environment, the actions these other agents took,
the rewards they received for this, nor the location they occupy in the state space.
We compare 2 reinforcement learning algorithms, i.e. learning automata and Q-learning
and show experimentally that in spatial coordination problems under study the
automata converge to a nash equilibrium in the limiting game.
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